Zenodo (CERN European Organization for Nuclear Research), May 26, 2023
Extracting the data from real-life speech and recognizing emotions from them is one of the challe... more Extracting the data from real-life speech and recognizing emotions from them is one of the challenging tasks. This has gained popularity over the past few years. The goal of this study is to have a direct human-computer interaction (HCI) to determine the person's condition or emotion through analyzing voice. The main purpose of this study is to extract emotion from the recorded audio as well as from the analysis of text extracted from the audio which is integrated together in a precise manner. The multimodal integration of speech and text gives good results by observing the emotional state of a person. Machine learning and deep learning algorithms are used to determine the emotional state of a person. The result of this study shows the accuracy around 62%.
Zenodo (CERN European Organization for Nuclear Research), May 26, 2023
This work explores the challenging task of identifying emotions from the eyes through the analysi... more This work explores the challenging task of identifying emotions from the eyes through the analysis of nonverbal cues and facial expressions. The study investigates the various emotions that the eyes can convey and their significance in emotional expression, including happiness, sadness, anger, fear, surprise, and contempt. The practical applications of this research include the potential use of computer vision algorithms to analyze images or videos of the eye region to identify emotions. The coevolution theory suggests that humans and robots can collaborate to achieve a shared goal. Therefore, this study aims to develop algorithms that can identify variations in pupil size, eye movements, and other traits linked to various emotional states, such as arousal, surprise, and disgust. The results of the research show that these algorithms can accurately identify emotions from the eyes, and a human expert can validate their interpretations. The methods used in this study include analyzing facial expressions and nonverbal cues, developing computer vision algorithms, and working with human experts to validate the results. The conclusions drawn from this work suggest that emotional identification from the eyes is a promising area of research that has practical applications in various fields, such as robotics, psychology, and human-computer interaction.
Prediction of Telecommunication Network State Based on Knowledge Graphs
The article focuses on developing a new prediction method for telecommunications network state ba... more The article focuses on developing a new prediction method for telecommunications network state based on knowledge graphs. The article analyses the prediction methods supported by current network monitoring and management systems. The proposed prediction method is based on analysis of behavior of each end-user device that allows reach high accuracy of prediction. Knowledge graphs of the networks are build using domain knowledge and statistical data. They contain knowledge about the possible states of the elements of the networks along with the knowledge about possible transitions between them that can be presented in the form of prediction rules. These rules allow predict the state of the network for the defined timestamp in the future. In the case study the practical task of internet traffic prediction for a segment of cable TV operator network is considered. Conclusions are formulated, and the areas of further research are defined.
This paper 1 questions the feasibility of a strong (general) data-centric artificial intelligence... more This paper 1 questions the feasibility of a strong (general) data-centric artificial intelligence (AI). The disadvantages of this type of intelligence are discussed. As an alternative, the concept of co-evolutionary hybrid intelligence is proposed. It is based on the cognitive interoperability of man and machine. An analysis of existing approaches to the construction of cognitive architectures is given. An architecture that seamlessly incorporates a human into the loop of intelligent problem solving is considered. The article is organized as follows. The first part contains a critique of data-centric intelligent systems. The reasons why it is impossible to create a strong artificial intelligence based on this type of intelligence are indicated. The second part briefly presents the concept of co-evolutionary hybrid intelligence and shows its advantages. The third part gives an overview and analysis of existing cognitive architectures. It is concluded that many of them do not consider humans as part of the intelligent data processing process. The next part discusses the cognitive architecture for co-evolutionary hybrid intelligence, providing integration with humans. It finishes with general conclusions about the feasibility of developing intelligent systems with humans in the problem solving loop.
XAI Evaluation: Evaluating Black-Box Model Explanations for Prediction
The results of evaluating explanations of the black-box model for prediction are presented. The X... more The results of evaluating explanations of the black-box model for prediction are presented. The XAI evaluation is realized through the different principles and characteristics between black-box model explanations and XAI labels. In the field of high-dimensional prediction, the black-box model represented by neural network and ensemble models can predict complex data sets more accurately than traditional linear regression and white-box models such as the decision tree model. However, an unexplainable characteristic not only hinders developers from debugging but also causes users mistrust. In the XAI field dedicated to “opening” the black box model, effective evaluation methods are still being developed. Within the established XAI evaluation framework (MDMC) in this paper, explanation methods for the prediction can be effectively tested, and the identified explanation method with relatively higher quality can improve the accuracy, transparency, and reliability of prediction.
Software engineering is an interactive, collaborative and creative activity that cannot be entire... more Software engineering is an interactive, collaborative and creative activity that cannot be entirely planned. Inspection and adaption are required to cope with changes during the development process. Software engineering education requires practical application of knowledge, but it is challenging and time consuming for instructors to evaluate the creation of innovative solutions to problems. Current higher education practices lead to a multitude of rules, guidelines and order. Instructors see deviations of students as failures and limit the creative thinking processes of students. In this paper we describe chaordic learning, a self-organizing, adaptive and nonlinear learning approach, to stimulate the creative thinking of students. Instructors provide structure and guidance, but also integrate freedom for self-organization and self-guided learning and embrace innovation and creativity. Deviations are seen as opportunities and failures as possibilities for students to learn and improve. We introduced chaordic learning into a games development course and a joint advanced student school and describe the chaordic process of these courses as case studies. Students in these courses report about an increased intrinsic motivation, a higher level of self-organization and more room for creativity leading to an improved learning experience and more fun.
Logistics Service Development based on the Geo2Tag and Smart-M3 Platforms
DOAJ (DOAJ: Directory of Open Access Journals), Apr 1, 2016
In this article we will talk about implementation of a logiltics service for a car fleet manageme... more In this article we will talk about implementation of a logiltics service for a car fleet management based on LBS GeolTag and Smart Space Smart-MJ platforms. Each platform has its own role in the work of logistics service. AU car fleet location information is stored and processed by the GeolTag platform. A Smart-MJ platform performs service metadata analysis in the form of service ontology and knowledge reasoning of these data. This work is focused on analysis and development of a logiltics car fleet management service using GeolTag and Smart-MJ platforms. Currently, there are a different classes of geographic data processing applications of road traffic objects from In-Car LBS system, fleet management systems to a satellite monitoring system for vehicles of the country. The current solution will create a context-aware system for processing and reasoning smart space subjects geographic data using LBS GeolTag platform. The system will consist of a variety types of agents or services for a fleet management from various angles - manager,driver and client.
Energy Aware Handover in LBS Interference for 5G Dense Heterogeneous Network
DOAJ (DOAJ: Directory of Open Access Journals), Nov 1, 2019
The key performance metrics for the Next gener- ation wireless communication system is the Energy... more The key performance metrics for the Next gener- ation wireless communication system is the Energy efficiency. We use dense Heterogenous Networks(HetNets) which comprises of Macro cell and Small cell where small cells include femto cell, pico cell, and micro cells. The femto cells are deployed in the macrocell area to improve the indoor coverage and provide better user experience. The mass deployment of femtocell faces a number of challenges among which interference management is very important. In this paper we consider the energy aware handovers between macro and femto base stations where the user equipment changes its services from one base station to another since the target one will offer better quality of performance. Handover decision can either be mobile initiated or network initiated and this decision is truly based on the received signal strength (RSS). The experiment is carried out using Network Simulator NS-3 and the results show that the proposed solution exhibits better performance in terms of quality of service metrics.
In the context of digital informatization, the Internet is changing the way of human existence. T... more In the context of digital informatization, the Internet is changing the way of human existence. The rapid development of the Internet has promoted the use of smartphones in people's daily lives, and at the same time, a large number of applications running on different operating system environments have appeared on the market. Predicting the duration of application usage is crucial for the management planning of related companies and the good life of users. In this work, a dataset containing time series of user application usage information is considered and the problem of "application usage" forecast is being addressed. The dataset used in this work is based on reliable and realistic user records of the usage of the application. Firstly, this paper investigates suitable forecast models for application development on the applied user usage time dataset, which includes neural network algorithms and ensemble algorithms, among others. Then, an Explainable Artificial Intelligence Approach (SHAP) is introduced to explain the selected optimal forecast models, thus enhancing user trust of the forecasting models. The forecast results show that the ensemble models perform better in the time series dataset of user application usage information, especially LightGBM has more obvious advantages. Explanation results show that the frequency of use of the target variables, category and lagged nature are important features in the forecast of the application dataset.
Congestion Avoidance and Delay Minimization in Energy Aware Routing of Dynamic ieee 802.11s WMN
IGI Global eBooks, 2020
Minimization of delay in collecting the data at any base stations is one of the major concerns in... more Minimization of delay in collecting the data at any base stations is one of the major concerns in cluster based Wireless Mesh Networks. several researches have proposed algorithms to control congestion considering static nature of a node. Mobility of a node results in high congestion due to frequent link breakages and high energy consumption due to re-establishment of route during routing process. Hence, the authors consider dynamic nodes with single hop inside the static cluster. The proposed model includes four modules namely, Cluster head selection, slot allocation, slot scheduling and data collection process. the cluster head selection is based on the maximum energy, number of links and link duration. Slot allocation is based on the available energy () and the required energy (). Slot scheduling is carried out based on the link duration. Data at the base station will be collected as they are scheduled. Model is tested using Network Simulator-3 (NS3) and results indicate that the proposed model achieves least delay besides reducing the congestion compared to the existing methods.
Zenodo (CERN European Organization for Nuclear Research), May 26, 2023
Extracting the data from real-life speech and recognizing emotions from them is one of the challe... more Extracting the data from real-life speech and recognizing emotions from them is one of the challenging tasks. This has gained popularity over the past few years. The goal of this study is to have a direct human-computer interaction (HCI) to determine the person's condition or emotion through analyzing voice. The main purpose of this study is to extract emotion from the recorded audio as well as from the analysis of text extracted from the audio which is integrated together in a precise manner. The multimodal integration of speech and text gives good results by observing the emotional state of a person. Machine learning and deep learning algorithms are used to determine the emotional state of a person. The result of this study shows the accuracy around 62%.
Zenodo (CERN European Organization for Nuclear Research), May 26, 2023
This work explores the challenging task of identifying emotions from the eyes through the analysi... more This work explores the challenging task of identifying emotions from the eyes through the analysis of nonverbal cues and facial expressions. The study investigates the various emotions that the eyes can convey and their significance in emotional expression, including happiness, sadness, anger, fear, surprise, and contempt. The practical applications of this research include the potential use of computer vision algorithms to analyze images or videos of the eye region to identify emotions. The coevolution theory suggests that humans and robots can collaborate to achieve a shared goal. Therefore, this study aims to develop algorithms that can identify variations in pupil size, eye movements, and other traits linked to various emotional states, such as arousal, surprise, and disgust. The results of the research show that these algorithms can accurately identify emotions from the eyes, and a human expert can validate their interpretations. The methods used in this study include analyzing facial expressions and nonverbal cues, developing computer vision algorithms, and working with human experts to validate the results. The conclusions drawn from this work suggest that emotional identification from the eyes is a promising area of research that has practical applications in various fields, such as robotics, psychology, and human-computer interaction.
Prediction of Telecommunication Network State Based on Knowledge Graphs
The article focuses on developing a new prediction method for telecommunications network state ba... more The article focuses on developing a new prediction method for telecommunications network state based on knowledge graphs. The article analyses the prediction methods supported by current network monitoring and management systems. The proposed prediction method is based on analysis of behavior of each end-user device that allows reach high accuracy of prediction. Knowledge graphs of the networks are build using domain knowledge and statistical data. They contain knowledge about the possible states of the elements of the networks along with the knowledge about possible transitions between them that can be presented in the form of prediction rules. These rules allow predict the state of the network for the defined timestamp in the future. In the case study the practical task of internet traffic prediction for a segment of cable TV operator network is considered. Conclusions are formulated, and the areas of further research are defined.
This paper 1 questions the feasibility of a strong (general) data-centric artificial intelligence... more This paper 1 questions the feasibility of a strong (general) data-centric artificial intelligence (AI). The disadvantages of this type of intelligence are discussed. As an alternative, the concept of co-evolutionary hybrid intelligence is proposed. It is based on the cognitive interoperability of man and machine. An analysis of existing approaches to the construction of cognitive architectures is given. An architecture that seamlessly incorporates a human into the loop of intelligent problem solving is considered. The article is organized as follows. The first part contains a critique of data-centric intelligent systems. The reasons why it is impossible to create a strong artificial intelligence based on this type of intelligence are indicated. The second part briefly presents the concept of co-evolutionary hybrid intelligence and shows its advantages. The third part gives an overview and analysis of existing cognitive architectures. It is concluded that many of them do not consider humans as part of the intelligent data processing process. The next part discusses the cognitive architecture for co-evolutionary hybrid intelligence, providing integration with humans. It finishes with general conclusions about the feasibility of developing intelligent systems with humans in the problem solving loop.
XAI Evaluation: Evaluating Black-Box Model Explanations for Prediction
The results of evaluating explanations of the black-box model for prediction are presented. The X... more The results of evaluating explanations of the black-box model for prediction are presented. The XAI evaluation is realized through the different principles and characteristics between black-box model explanations and XAI labels. In the field of high-dimensional prediction, the black-box model represented by neural network and ensemble models can predict complex data sets more accurately than traditional linear regression and white-box models such as the decision tree model. However, an unexplainable characteristic not only hinders developers from debugging but also causes users mistrust. In the XAI field dedicated to “opening” the black box model, effective evaluation methods are still being developed. Within the established XAI evaluation framework (MDMC) in this paper, explanation methods for the prediction can be effectively tested, and the identified explanation method with relatively higher quality can improve the accuracy, transparency, and reliability of prediction.
Software engineering is an interactive, collaborative and creative activity that cannot be entire... more Software engineering is an interactive, collaborative and creative activity that cannot be entirely planned. Inspection and adaption are required to cope with changes during the development process. Software engineering education requires practical application of knowledge, but it is challenging and time consuming for instructors to evaluate the creation of innovative solutions to problems. Current higher education practices lead to a multitude of rules, guidelines and order. Instructors see deviations of students as failures and limit the creative thinking processes of students. In this paper we describe chaordic learning, a self-organizing, adaptive and nonlinear learning approach, to stimulate the creative thinking of students. Instructors provide structure and guidance, but also integrate freedom for self-organization and self-guided learning and embrace innovation and creativity. Deviations are seen as opportunities and failures as possibilities for students to learn and improve. We introduced chaordic learning into a games development course and a joint advanced student school and describe the chaordic process of these courses as case studies. Students in these courses report about an increased intrinsic motivation, a higher level of self-organization and more room for creativity leading to an improved learning experience and more fun.
Logistics Service Development based on the Geo2Tag and Smart-M3 Platforms
DOAJ (DOAJ: Directory of Open Access Journals), Apr 1, 2016
In this article we will talk about implementation of a logiltics service for a car fleet manageme... more In this article we will talk about implementation of a logiltics service for a car fleet management based on LBS GeolTag and Smart Space Smart-MJ platforms. Each platform has its own role in the work of logistics service. AU car fleet location information is stored and processed by the GeolTag platform. A Smart-MJ platform performs service metadata analysis in the form of service ontology and knowledge reasoning of these data. This work is focused on analysis and development of a logiltics car fleet management service using GeolTag and Smart-MJ platforms. Currently, there are a different classes of geographic data processing applications of road traffic objects from In-Car LBS system, fleet management systems to a satellite monitoring system for vehicles of the country. The current solution will create a context-aware system for processing and reasoning smart space subjects geographic data using LBS GeolTag platform. The system will consist of a variety types of agents or services for a fleet management from various angles - manager,driver and client.
Energy Aware Handover in LBS Interference for 5G Dense Heterogeneous Network
DOAJ (DOAJ: Directory of Open Access Journals), Nov 1, 2019
The key performance metrics for the Next gener- ation wireless communication system is the Energy... more The key performance metrics for the Next gener- ation wireless communication system is the Energy efficiency. We use dense Heterogenous Networks(HetNets) which comprises of Macro cell and Small cell where small cells include femto cell, pico cell, and micro cells. The femto cells are deployed in the macrocell area to improve the indoor coverage and provide better user experience. The mass deployment of femtocell faces a number of challenges among which interference management is very important. In this paper we consider the energy aware handovers between macro and femto base stations where the user equipment changes its services from one base station to another since the target one will offer better quality of performance. Handover decision can either be mobile initiated or network initiated and this decision is truly based on the received signal strength (RSS). The experiment is carried out using Network Simulator NS-3 and the results show that the proposed solution exhibits better performance in terms of quality of service metrics.
In the context of digital informatization, the Internet is changing the way of human existence. T... more In the context of digital informatization, the Internet is changing the way of human existence. The rapid development of the Internet has promoted the use of smartphones in people's daily lives, and at the same time, a large number of applications running on different operating system environments have appeared on the market. Predicting the duration of application usage is crucial for the management planning of related companies and the good life of users. In this work, a dataset containing time series of user application usage information is considered and the problem of "application usage" forecast is being addressed. The dataset used in this work is based on reliable and realistic user records of the usage of the application. Firstly, this paper investigates suitable forecast models for application development on the applied user usage time dataset, which includes neural network algorithms and ensemble algorithms, among others. Then, an Explainable Artificial Intelligence Approach (SHAP) is introduced to explain the selected optimal forecast models, thus enhancing user trust of the forecasting models. The forecast results show that the ensemble models perform better in the time series dataset of user application usage information, especially LightGBM has more obvious advantages. Explanation results show that the frequency of use of the target variables, category and lagged nature are important features in the forecast of the application dataset.
Congestion Avoidance and Delay Minimization in Energy Aware Routing of Dynamic ieee 802.11s WMN
IGI Global eBooks, 2020
Minimization of delay in collecting the data at any base stations is one of the major concerns in... more Minimization of delay in collecting the data at any base stations is one of the major concerns in cluster based Wireless Mesh Networks. several researches have proposed algorithms to control congestion considering static nature of a node. Mobility of a node results in high congestion due to frequent link breakages and high energy consumption due to re-establishment of route during routing process. Hence, the authors consider dynamic nodes with single hop inside the static cluster. The proposed model includes four modules namely, Cluster head selection, slot allocation, slot scheduling and data collection process. the cluster head selection is based on the maximum energy, number of links and link duration. Slot allocation is based on the available energy () and the required energy (). Slot scheduling is carried out based on the link duration. Data at the base station will be collected as they are scheduled. Model is tested using Network Simulator-3 (NS3) and results indicate that the proposed model achieves least delay besides reducing the congestion compared to the existing methods.
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Papers by Kirill Krinkin